Transcript Getting to the essential

The third factor
Effect modification
Confounding factor
FETP India
Competency to be gained
from this lecture
Identify and describe an effect modification
Eliminate a confounding factor
Key elements
• Describing an effect modification
• Eliminating a confounding factor
Stratification
• Sub-groups can be defined according to
various characteristics in a population
 Age
 Sex
 Socio-economic status
• An association between a risk factor and an
outcome may be studied within these various
strata
Key elements
• Describing an effect modification
• Eliminating a confounding factor
Effect modification
Spotting effect modification
in a stratified analysis
• Effect modification (= Interaction) occurs
when the answer about a measure of
association is:
 “it depends”
• Examples:
 Efficacy of measles vaccine
• Variation according to the age
 Risk of myocardial infarction among women
taking oral contraceptives
• Variation according to smoking habits
Effect modification
Describing an effect modification
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Conduct crude analysis
Stratify data by suspected modifier
Observe the association strata by strata
Judge the heterogeneity of:
 Odds ratios
 Relative risks
• Test a potential difference
• Report the effect modification
Effect modification
Describing an effect modification
•
•
•
•
Conduct crude analysis
Stratify data by suspected modifier
Observe the association strata by strata
Judge the heterogeneity of:
 Odds ratios
 Relative risks
• Test a potential difference
• Report the effect modification
Effect modification
Describing an effect modification
•
•
•
•
Conduct crude analysis
Stratify data by suspected modifier
Observe the association strata by strata
Judge the heterogeneity of:
 Odds ratios
 Relative risks
• Test a potential difference
• Report the effect modification
Effect modification
Death from diarrhoea according to
breast- feeding, Brazil, 1980s
(Crude analysis)
Diarrhoea
Controls
Total
No breastfeeding
120
136
256
Breastfeeding
50
204
254
Total
170
340
510
Odds ratio: 3.6; 95% CI: 2.4- 5.5; p < 0.0001
Effect modification
Describing an effect modification
Conduct crude analysis
Stratify data by suspected modifier
Observe the association strata by strata
Judge the heterogeneity of:
 Odds ratios
 Relative risks
• Test a potential difference
• Report the effect modification
•
•
•
•
Effect modification
Death from diarrhoea according
to breastfeeding, Brazil, 1980s
Infants < 1 month of age
Cases
Controls
Total
No breastfeeding
10
3
13
Breastfeeding
7
68
75
Total
17
71
88
Cases
Controls
Total
No breastfeeding
110
133
243
Breastfeeding
43
136
179
Total
153
269
422
Infants ≥ 1 month of age
Describing an effect modification
•
•
•
•
Conduct crude analysis
Stratify data by suspected modifier
Observe the association strata by strata
Judge the heterogeneity of:
 Odds ratios
 Relative risks
• Test a potential difference
• Report the effect modification
Effect modification
Death from diarrhoea according
to breast feeding, Brazil, 1980s:
Analysis among infants < 1 month of age
Cases
Controls
Total
No breastfeeding
10
3
13
Breastfeeding
7
68
75
Total
17
71
88
Odds ratio: 32.4; 95% CI: 6- 203; p < 0.0001
Effect modification
Death from diarrhoea according
to breast feeding, Brazil, 1980s:
Analysis among infants ≥ 1 month of age
Cases
Controls
Total
No breastfeeding
110
133
243
Breastfeeding
43
136
179
Total
153
269
422
Odds ratio: 2.6; 95% CI: 1.7- 4.1; p < 0.0001
Effect modification
Describing an effect modification
•
•
•
•
Conduct crude analysis
Stratify data by suspected modifier
Observe the association strata by strata
Judge the heterogeneity of:
 Odds ratios
 Relative risks
• Test a potential difference
• Report the effect modification
Effect modification
Judge the heterogeneity of the
measures of association
• To be a difference, a difference should make
a difference
 Review public health implications
• Odds ratios in the specific example:
 Strata 1: OR = 32; 95% CI: 6.0- 200
 Strata 2: OR = 2.6; 95% CI: 1.7- 4.1
Effect modification
Describing an effect modification
•
•
•
•
Conduct crude analysis
Stratify data by suspected modifier
Observe the association strata by strata
Judge the heterogeneity of:
 Odds ratios
 Relative risks
• Test a potential difference
• Report the effect modification
Effect modification
Woolf’s test for heterogeneity
of the odds ratios
• Statistical testing of the heterogeneity of
the odds ratios
• Lacks statistical power
• Calculation:
 In statistical textbooks
 In the software’s analysis output
• Judgement is important
Effect modification
Handling heterogeneous
measures of association
ORs / RRs are
different across strata
ORs / RRs 95% C.I.
do not overlap
ORs / RRs C.I.
do overlap
Effect modification
Use Woolf's test
Woolf's test significant
Woolf's test not significant
Effect modification
Effect modification
unlikely
Discuss lack of power
of Wollf's test
Describing an effect modification
•
•
•
•
Conduct crude analysis
Stratify data by suspected modifier
Observe the association strata by strata
Judge the heterogeneity of:
 Odds ratios
 Relative risks
• Test a potential difference
• Report the effect modification
Effect modification
Conclusion of the Brazilian case-control
study on breastfeeding
and death from diarrhoea
• The protective efficacy of breastfeeding is
more marked among infants under the age of
one month
• This may correspond to a biological
phenomenon that must be reported as part
of the results
Effect modification
Reporting results in the presence of
an effect modification
• Once the effect modification was detected
the study population is split
• Results for the risk factor considered are
reported stratum by stratum
Effect modification
Vaccination against hepatitis B among
institutionalized children in Romania
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Hepatitis B is highly endemic in Romania
Many children live in institutions
Institutionalized children are at higher risk
1995: Hepatitis B immunization initiated
1997: Evaluation through serologic survey
Effect modification
Hepatitis B vaccine efficacy among
institutionalized children over 6 months
of age *, Romania, 1997
HBV
Vaccine
Anti-HBc (+) Anti-HBc (-) RR
3 doses
15
383
0.48
< 3 doses
4
47
Ref.
95% C.I.
0.17-1.4
Vaccine efficacy, 52%, 95% CI 0-83%
* Born after implementation of routine vaccination
Effect modification
Others
District X
Hepatitis B vaccine efficacy among
institutionalized children over 6 months
of age *, by district, Romania, 1997
Anti-HBc (+) Anti-HBc (-) RR
3 doses
12
61
2.0
< 3 doses
1
11
Ref.
95% C.I.
0.28-14
3 doses
< 3 doses
0.0-0.6
3
3
322
36
0.12
Ref.
Wolf test for evaluation of interaction: p = 0.03
* Born after implementation of routine vaccination
Effect modification
Hepatitis B vaccine efficacy among
Romanian children in institutions:
Conclusions
• The protective efficacy of hepatitis B
vaccine appears low overall
• This overall low efficacy does not correspond
to a biological phenomenon
• In fact, the efficacy is:
 Normal in most districts (88%)
 Low in district X
• This points towards programme errors that
must be identified and prevented
Effect modification
Describing an effect modification:
Summary
• The analysis plan:
 Anticipates effect modifiers to collect data
• The analysis:
 Looks for effect modification to test it
• The report:
 Breaks down the population in strata to report
the effect modification
Effect modification
Key elements
• Describing an effect modification
• Eliminating a confounding factor
Confounding factor
What may explain an association
between a risk factor and an outcome?
?
?
?
?
Chance
Bias
Third factor
Causal association
Confounding factor
What may explain an association
between a risk factor and an outcome?
?
?
?
?
Chance
Bias
Third factor
Causal association
Confounding factor
Characteristics of a third,
confounding factor
• Associated with the exposure
 Without being a consequence of exposure
• Associated with the outcome
 Independently from the exposure
Exposure
Outcome
Confounding factor
Confounding factor
The nuisance introduced by
confounding factors
• May simulate an association
• May hide an association that does exist
• May alter the strength of the association
 Increased
 Decreased
Confounding factor
Example of confounding factor
Apparent association
Exposure 1
Outcome
Confounding
factor
Confounding factor
Example of confounding factor (1)
Apparent association
Ethnicity
Pneumonia
Crowding
Confounding factor
Example of confounding factor (2)
Altered strength of association
Crowding
Pneumonia
Malnutrition
Confounding factor
Eliminating confounding in the
pneumonia example
• Estimate the strength of the association
between malnutrition and pneumonia
• Estimate the strength of the association
between crowding and pneumonia
 Adjusted for the effect of malnutrition
• Eliminate the confounding effect of crowding
on the false association between ethnicity
and pneumonia
Confounding factor
Controlling a confounding factor
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Stratification
Restriction
Matching
Randomization
Multivariate analysis
Confounding factor
Controlling a confounding factor
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Stratification
Restriction
Matching
Randomization
Multivariate analysis
Confounding factor
Adjustment to eliminate confounding
• Examine strength of association across strata
• Check for the absence of effect modification
 If there is an effect modification, break in
various strata, report. End of the story
• Observation of a strength of association:
 Homogeneous across strata
 Different from the crude measure
• Calculate weighted average of stratumspecific measures of association
Confounding factor
Malaria and radio sets
• Hypothesis: Could radio waves be a repellent
for female anopheles?
• Cohort study on the risk factors for malaria
in an endemic area
Confounding factor
Incidence of malaria according to the
presence of a radio set,
Kahinbhi Pradesh
Crude data
Malaria
No malaria
Total
Radio
80
440
520
No radio
220
860
1080
Total
300
1300
1600
RR: 0.7; 95% CI: 0.6- 0.9; p < 0.02
Confounding factor
Incidence of malaria according to the
presence of a radio set,
Kahinbhi Pradesh
Strata 1: Sleeping under a mosquito net
Malaria
No malaria
Total
Radio
30
370
400
No radio
50
630
680
Total
80
1000
1080
RR: 1.02; 95% CI: 0.7- 1.6; p < 0.97
Confounding factor
Incidence of malaria according to the
presence of a radio set,
Kahinbhi Pradesh
Strata 2: Sleeping without a mosquito net
Malaria No malaria
Total
Radio
50
70
120
No radio
170
230
400
Total
220
300
520
RR: 0.98; 95% CI: 0.8- 1.2; p < 0.95
Confounding factor
Mantel-Haenszel adjusted relative risk
aixL0i) / Ti]
RR M-H=
ci xL1i) / Ti]
Confounding factor
Malaria and radio sets:
Conclusion
• No association between radio and malaria
within each strata
• The new adjusted relative risk replaces the
crude one Apparent association
Radio sets
Malaria
Mosquito nets
Confounding factor
Mantel-Haenszel adjusted odds ratio
ai.di) / Ti]
OR M-H=
bi.ci) / Ti]
Confounding factor
Controlling a confounding factor
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Stratification
Restriction
Matching
Randomization
Multivariate analysis
Confounding factor
Hepatitis B and blood transfusion in
Moldova
• Hepatitis B virus infection is highly endemic
in Moldova
• Routes of transmission are unknown
• A case control study was initiated to assess
potential modes of transmission
Confounding factor
Acute hepatitis B and receiving a
transfusion in Moldova, 1994-1995
Cases
Controls
Total
Transfusion
3
1
4
Non-transfusion
69
189
258
Total
72
190
262
Odds ratio: 8.2; 95% CI : 0.8-220
Confounding factor
Acute hepatitis B and receiving a
transfusion in Moldova, 1994-1995
(According to receiving injections)
Injections
No injections
Case
Control
Total
Transfusion
0
0
0
28
No transfusion
47
183
230
32
Total
47
183
230
Case
Control
Total
Transfusion
3
1
6
No transfusion
22
6
Total
25
7
Odds ratio: Odds ratio: 0.8,
95% CI: 0.1-24.9
Confounding factor
Controlling a confounding factor
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Stratification
Restriction
Matching
Randomization
Multivariate analysis
Confounding factor
Matching
• Stratification conducted initially at the stage
of the study design of a case control study
• Stratified analysis (matched) necessary
Confounding factor
Controlling a confounding factor
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Stratification
Restriction
Matching
Randomization
Multivariate analysis
Confounding factor
Randomization
• Distribution of exposure of interest at
random in the study population for a
prospective cohort
• An association between an exposure and a
confounding factor will be:
 Secondary to chance alone
 Improbable
Confounding factor
Controlling a confounding factor
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•
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Stratification
Restriction
Matching
Randomization
Multivariate analysis
Confounding factor
Multivariate analysis
• Mathematical model
• Simultaneous adjustment of all confounding
and risk factors
• Can address effect modification
Confounding factor
Taking into account a third factor
in practice
1.
2.
3.
4.
5.
Think of potential confounding factors
Collect accurate data on them
Conduct crude analysis
Stratify
Look for effect modification
•
Are the RR or OR different to each other?
6. If effect modification:
•
•
Report
Do not adjust
7. Control confounding factors through
adjustment
•
Before
the study
If applicable
During
the analysis
Analyzing a third factor
Examine crude OR / RR
Examine ORs / RRs in each stratum
Identical ORs / RRs across strata
Different ORs / RRs across strata
Strata ORs / RRs similar to crude
(Crude value falls between strata)
Strata ORs / RRs different from crude
(Crude value does not fall between strata)
Effect modification
Third factor does not play a role
Confounding factor
Stop the analysis.
DO NOT adjust!
Report ONE crude OR/RR
Adjust using the
M-H technique
Report MULTIPLE ORs / RRs
for each stratum
Eliminate the confouding
Report ONE adjusted OR / RR
Take-home messages
• Describe effect modifications
 The analysis must TEST for their occurrences
• Control confounding factors
 The analysis must ELIMINATE their influence